{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Packages Loaded\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import csv\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential,  Model\n",
    "from keras.layers.core import Dense, Flatten, Activation, Dropout, Lambda\n",
    "from keras.layers.convolutional import Convolution2D, Cropping2D\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.misc import imread\n",
    "import matplotlib.pyplot as plt\n",
    "from math import *\n",
    "import cv2\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "import keras\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "print('All Packages Loaded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Data Loaded\n"
     ]
    }
   ],
   "source": [
    "x = pd.read_csv('/home/carnd/test/data/driving_log.csv')\n",
    "print(\"All Data Loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T08jJk+FXlKcy8JZMkUKU6vG1ZDY2PgW4uXySKJUIvfewU8k8ceIJxo9/JYWF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\n29to8yokp3yBMTWoouLlGYUas1NQUEqfbdZBFHd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Z8adwXoU2mXn7gZNN84+411LcdNmV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f708aa5fc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.steering.plot(title='Steering data distribution', fontsize=17, figsize=(10,4), color= 'y')\n",
    "plt.xlabel('frames')\n",
    "plt.ylabel('Steering angle')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m1tala/4AHn4YdtoJNt+8+DpJs2+al3czmxrS92QyFdTWW48mL52UaQj5Sck0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\n/x910E3ts9VjuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70a4516f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.throttle.plot(title='Throttle data distribution', fontsize=17, figsize=(10,4), color= 'b')\n",
    "plt.xlabel('frames')\n",
    "plt.ylabel('Throttle')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jga2Bo4BnAccDT6g+3B5bkiRpEcpSrqNWrlxZq1evXugwJEmSNirJeVW1ctI/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\n4k+SJGlALP4kSZIGxOJPkiRpQP5/sQQ25pZSA5wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7089bd5780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.brake.plot(title='Brake data distribution', fontsize=17, figsize=(10,4), color= 'k')\n",
    "plt.xlabel('frames')\n",
    "plt.ylabel('Brake')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yJNlHL7EyRSsB23z10Vvsrh/NTAytSoVIdhSGC8FsARNeuwSaMnqLQxdEkfxI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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7089b3e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.speed.plot(title='Speed data distribution', fontsize=17, figsize=(10,4), color= 'm')\n",
    "plt.xlabel('frames')\n",
    "plt.ylabel('Speed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samples = [] \n",
    "\n",
    "with open('/home/carnd/test/data/driving_log.csv') as csvfile:\n",
    "    reader = csv.reader(csvfile)\n",
    "    next(reader, None) \n",
    "    for line in reader:\n",
    "        samples.append(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_samples, validation_samples = train_test_split(samples,test_size=0.15) #simply splitting the dataset to train and validation set usking sklearn. .15 indicates 15% of the dataset is validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def generator(samples, batch_size=32):\n",
    "    num_samples = len(samples)\n",
    "   \n",
    "    while 1: \n",
    "        shuffle(samples)\n",
    "        for offset in range(0, num_samples, batch_size):\n",
    "            \n",
    "            batch_samples = samples[offset:offset+batch_size]\n",
    "\n",
    "            images = []\n",
    "            angles = []\n",
    "            for batch_sample in batch_samples:\n",
    "                    for i in range(0,3):\n",
    "                        \n",
    "                        name = '/home/carnd/test/data/IMG/'+batch_sample[i].split('/')[-1]\n",
    "                        main_image = cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB) \n",
    "                        required_angle = float(batch_sample[3])\n",
    "                        images.append(main_image)\n",
    "                        \n",
    "                     \n",
    "                        \n",
    "                        if(i==0):\n",
    "                            angles.append(required_angle)\n",
    "                        elif(i==1):\n",
    "                            angles.append(required_angle+0.2)\n",
    "                        elif(i==2):\n",
    "                            angles.append(required_angle-0.2)\n",
    "                        \n",
    "                        \n",
    "                        \n",
    "                        images.append(cv2.flip(main_image,1))\n",
    "                        if(i==0):\n",
    "                            angles.append(required_angle*-1)\n",
    "                        elif(i==1):\n",
    "                            angles.append((required_angle+0.2)*-1)\n",
    "                        elif(i==2):\n",
    "                            angles.append((required_angle-0.2)*-1)\n",
    "                            \n",
    "                        \"\"\"    \n",
    "                        hsv = cv2.cvtColor(main_image, cv2.COLOR_RGB2HSV)\n",
    "                        ratio = 1.0 + 0.4 * (np.random.rand() - 0.5)\n",
    "                        hsv[:,:,2] =  hsv[:,:,2] * ratio\n",
    "                        img = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)\n",
    "                        images.append(img)\n",
    "                        \n",
    "                        if(i==0):\n",
    "                            angles.append(required_angle*-1)\n",
    "                        elif(i==1):\n",
    "                            angles.append((required_angle+0.2)*-1)\n",
    "                        elif(i==2):\n",
    "                            angles.append((required_angle-0.2)*-1)\n",
    "                        \"\"\"\n",
    "                          \n",
    "                        \n",
    "        \n",
    "            X_train = np.array(images)\n",
    "            y_train = np.array(angles)\n",
    "            \n",
    "            yield sklearn.utils.shuffle(X_train, y_train)\n",
    "            \n",
    "\n",
    "\n",
    "train_generator = generator(train_samples, batch_size=32)\n",
    "validation_generator = generator(validation_samples, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Type of Problem : Regression\n",
    "### Problem Statement: Find the Steering angle, given images from three cameras, i.e Left, Centre and Right.\n",
    "\n",
    "##### Checklist for creating the pipeline of the NVIDIA model : https://tinyurl.com/y88xelpb\n",
    "1. Preprocess incoming data, centered around zero with small standard deviation \n",
    "2. Trim image the to train the network on the basis of the road and the steering angle only.\n",
    "3. Layer 1 : Convolution Layer, Number of Filters : 24, Filter size= 5x5,  stride= 2x2.\n",
    "4. Layer 2 : Convolution Layer, Number of Filters : 36, Filter Size = 5x5, stride = 2x2.\n",
    "5. Layer 3 : Convolution Layer, Number of Filters : 48, Filter Size = 5x5, stride = 2x2.\n",
    "6. Layer 4 : Convolution Layer, Number of Filters : 64, Filter Size = 3x3, stride = 1x1.\n",
    "7. Layer 5 : Convolution Layer, Number of Filters : 64, Filter Size = 3x3, stride = 1x1.\n",
    "8. Flatten the Image from a 2D format to a row based array.\n",
    "9. Layer 6 : Fully Connected Layer\n",
    "10. A dropout layer to avoid overfitting.\n",
    "11. Layer 7 : Fully Connected Layer\n",
    "12. Layer 8 : Fully Connected Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "Normalization (Lambda)           (None, 160, 320, 3)   0           lambda_input_5[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "cropping2d_5 (Cropping2D)        (None, 75, 320, 3)    0           Normalization[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "Conv1 (Convolution2D)            (None, 36, 158, 24)   1824        cropping2d_5[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "Conv2 (Convolution2D)            (None, 16, 77, 36)    21636       Conv1[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "Conv3 (Convolution2D)            (None, 6, 37, 48)     43248       Conv2[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "Conv4 (Convolution2D)            (None, 4, 35, 64)     27712       Conv3[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "Conv5 (Convolution2D)            (None, 2, 33, 64)     36928       Conv4[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "flatten_5 (Flatten)              (None, 4224)          0           Conv5[0][0]                      \n",
      "____________________________________________________________________________________________________\n",
      "FC1 (Dense)                      (None, 50)            211250      flatten_5[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "FC2 (Dense)                      (None, 10)            510         FC1[0][0]                        \n",
      "____________________________________________________________________________________________________\n",
      "output (Dense)                   (None, 1)             11          FC2[0][0]                        \n",
      "====================================================================================================\n",
      "Total params: 343,119\n",
      "Trainable params: 343,119\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Lambda(lambda x: x/127.5 - 1., input_shape=(160,320, 3), name='Normalization'))\n",
    "model.add(Cropping2D(cropping=((60, 25),(0,0))))\n",
    "model.add(Convolution2D(24, 5, 5, subsample=(2,2), activation='elu', name='Conv1'))\n",
    "model.add(Convolution2D(36, 5, 5, subsample=(2,2), activation='elu', name='Conv2'))\n",
    "model.add(Convolution2D(48, 5, 5, subsample=(2,2), activation='elu', name='Conv3'))\n",
    "model.add(Convolution2D(64, 3, 3, activation='relu', name='Conv4'))\n",
    "model.add(Convolution2D(64, 3, 3, activation='relu', name='Conv5'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(50, activation='elu', name='FC1'))\n",
    "model.add(Dense(10, activation='elu', name='FC2'))\n",
    "model.add(Dense(1, name='output'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LossHistory(keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        print('BEGIN TRAINING')\n",
    "        self.losses = []\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        self.losses.append(logs.get('loss'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',\n",
    "                                 monitor='val_loss',\n",
    "                                 verbose=0,\n",
    "                                 save_best_only=True,\n",
    "                                 mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_history = LossHistory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Early_Stopping=keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Tensor_Board = keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=0, write_graph=True, write_images=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/keras/callbacks.py:618 in set_model.: merge_all_summaries (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.\n",
      "Instructions for updating:\n",
      "Please switch to tf.summary.merge_all.\n",
      "BEGIN TRAINING\n",
      "Epoch 1/5\n",
      "6720/6830 [============================>.] - ETA: 0s - loss: 0.0424"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/keras/engine/training.py:1569: UserWarning: Epoch comprised more than `samples_per_epoch` samples, which might affect learning results. Set `samples_per_epoch` correctly to avoid this warning.\n",
      "  warnings.warn('Epoch comprised more than '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6912/6830 [==============================] - 15s - loss: 0.0418 - val_loss: 0.0235\n",
      "Epoch 2/5\n",
      "6912/6830 [==============================] - 14s - loss: 0.0204 - val_loss: 0.0194\n",
      "Epoch 3/5\n",
      "6912/6830 [==============================] - 15s - loss: 0.0183 - val_loss: 0.0194\n",
      "Epoch 4/5\n",
      "6912/6830 [==============================] - 14s - loss: 0.0186 - val_loss: 0.0218\n",
      "Epoch 5/5\n",
      "6912/6830 [==============================] - 14s - loss: 0.0197 - val_loss: 0.0151\n",
      "Hurray! The Model has been Saved!\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='mse',optimizer='adam')\n",
    "plot_model = model.fit_generator(train_generator, samples_per_epoch= len(train_samples), validation_data=validation_generator,callbacks = [batch_history, Tensor_Board ],nb_val_samples=len(validation_samples), nb_epoch=5,verbose=1)\n",
    "model.save('model.h5')\n",
    "print('Hurray! The Model has been Saved!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f/OApjK0p5aZHV9IXwTc/cKov7jOzo4ID4wBJ4gsXnkiBxA2/eoXXtrXz5YtncsaUkYNd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\nHBhmZpaJA8PMzDL5/yqQBnrCdxHrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7089b10f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def running_mean(x, N):\n",
    "    cumsum = np.cumsum(np.insert(x, 0, 0)) \n",
    "    return (cumsum[N:] - cumsum[:-N]) / N \n",
    "\n",
    "def plot_history(history):\n",
    "    plt.plot(running_mean(history.losses, 50))\n",
    "    plt.ylim(0, 0.06)\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('batches')\n",
    "    plt.show()\n",
    "    \n",
    "plot_history(batch_history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AoeHOFZDRzWHk085NwSZo/L2z3Z9E8jLwDTABp5P9l0C4qo4NRKCV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\niSSpiUEiSWpikEiSmrwB5AyrgiY8PDsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7089b10940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(plot_model.history['loss'])\n",
    "plt.plot(plot_model.history['val_loss'])\n",
    "plt.title('model mean squared error loss')\n",
    "plt.ylabel('mean squared error loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['training set', 'validation set'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
